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Diagnostic evaluation of patients with asymptomatic disturbances of cardiac rhythm such as: -Dizziness -Heart race -Palpitation -Syncopesof unknown cause

The ECG monitoring devices are post-event recorders, also called tele-ECG devices, which realize one-channel (100BT) or three- channel (300BT) recordings of short ECG sections, their memorization as well as the preparation of the transmission. Therefore, the purpose of the recorders is the derivation and memorization of an ECG section in combination with a compatible telemedicine system. The recorder provides physicians with information used for the diagnosis of cardiac arrhythmias. The Post Event 300BT device is a modification to Vitaphone's previously cleared Post Event 110BT device cleared pursuant to K100383. The modification consists of the availability of three recording channels rather than one recording channel. There is no change to intended use, patient contact materials or core technology. The hardware of the 100BT and the 300BT is basically the same. The differences between both devices is the activation of the channels by the software. The device supports the transmission of the data. The recorders are battery operated devices. The recorders do not possess a separate on/off switch.

The provided document is a 510(k) summary for the Vitaphone Post-Event Recorder 300 BT. This document focuses on demonstrating substantial equivalence to a predicate device (Vitaphone Post Event Recorder 100BT - K100383) rather than defining specific performance acceptance criteria for a new AI/algorithm-driven device and a study to prove it.

The Vitaphone Post-Event Recorder 300 BT is a cardiac event recorder, not an AI or algorithm-driven diagnostic device in the sense of image analysis or complex pattern recognition. Its function is to record ECG signals and transmit them. The "performance data" section indicates compliance with safety and essential performance standards for medical electrical equipment.

Therefore, many of the requested points regarding AI/algorithm performance (e.g., sample size for test/training sets, ground truth establishment methods, expert adjudication, MRMC studies, standalone performance) are not applicable to this type of device and its 510(k) submission as presented.

Based on the provided text, here is what can be extracted and what cannot:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly present a table of "acceptance criteria" in the typical sense of diagnostic performance metrics (sensitivity, specificity, AUC). Instead, it relies on compliance with established medical device standards and functional equivalence to the predicate device.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Not applicable / Not provided. The document describes engineering validation and verification testing against standards, not a clinical study with a "test set" of patient data for diagnostic algorithm performance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Not applicable / Not provided. This is not an AI/algorithm for diagnostic interpretation. Ground truth for ECG signal quality would be assessed through technical means, not expert consensus on diagnoses.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Not applicable / Not provided. No such adjudication for diagnostic performance is mentioned.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• No. This is not an AI-assisted diagnostic device; therefore, no MRMC study or AI-assistance effect size is relevant or provided.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done

• Not applicable / No. The device is a recorder, not a standalone diagnostic algorithm. It enables healthcare professionals to acquire ECGs for later diagnostic evaluation.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

• Not applicable / Not provided. For a simple ECG recorder, the "ground truth" relates to the accurate acquisition and transmission of the electrical signals from the heart, which is verified against known electrical inputs and validated against performance standards rather than clinical ground truth like pathology for a diagnostic algorithm.

8. The sample size for the training set

• Not applicable / Not provided. This device is hardware with firmware, not a machine learning model that requires a "training set."

9. How the ground truth for the training set was established

• Not applicable / Not provided. As above, no training set for a machine learning model is involved.

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